Overview

Dataset statistics

Number of variables28
Number of observations102825
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.8 MiB
Average record size in memory232.0 B

Variable types

Numeric13
Categorical15

Alerts

Departure Delay in Minutes has 58649 (57.0%) zerosZeros
Arrival Delay in Minutes has 58135 (56.5%) zerosZeros

Reproduction

Analysis started2023-02-21 16:55:55.255911
Analysis finished2023-02-21 16:56:39.829170
Duration44.57 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Flight Distance
Real number (ℝ)

Distinct3798
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7025184
Minimum3.4657359
Maximum8.3532615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:39.921192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.4657359
5-th percentile5.170484
Q16.025866
median6.73578
Q37.4593389
95-th percentile8.1235578
Maximum8.3532615
Range4.8875256
Interquartile range (IQR)1.4334729

Descriptive statistics

Standard deviation0.9142734
Coefficient of variation (CV)0.13640744
Kurtosis-0.70864992
Mean6.7025184
Median Absolute Deviation (MAD)0.71893994
Skewness-0.20352535
Sum689186.45
Variance0.83589584
MonotonicityNot monotonic
2023-02-21T11:56:40.061245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.823045895 654
 
0.6%
6.388561406 395
 
0.4%
6.003887067 390
 
0.4%
6.760414691 368
 
0.4%
7.814399634 361
 
0.4%
6.104793232 358
 
0.3%
5.468060141 350
 
0.3%
5.991464547 330
 
0.3%
5.733341277 329
 
0.3%
5.262690189 328
 
0.3%
Other values (3788) 98962
96.2%
ValueCountFrequency (%)
3.465735903 8
 
< 0.1%
4.043051268 8
 
< 0.1%
4.219507705 124
0.1%
4.304065093 58
0.1%
4.317488114 30
 
< 0.1%
4.343805422 1
 
< 0.1%
4.356708827 41
 
< 0.1%
4.369447852 30
 
< 0.1%
4.394449155 2
 
< 0.1%
4.418840608 7
 
< 0.1%
ValueCountFrequency (%)
8.3532615 17
< 0.1%
8.294299609 11
< 0.1%
8.29404964 5
 
< 0.1%
8.293799609 8
< 0.1%
8.293549515 9
< 0.1%
8.293299359 8
< 0.1%
8.29304914 6
 
< 0.1%
8.292798858 6
 
< 0.1%
8.292548514 13
< 0.1%
8.292298107 6
 
< 0.1%
Distinct214
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1928978
Minimum0
Maximum5.3752784
Zeros58649
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:40.205176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5649494
95-th percentile4.2484952
Maximum5.3752784
Range5.3752784
Interquartile range (IQR)2.5649494

Descriptive statistics

Standard deviation1.5700364
Coefficient of variation (CV)1.3161533
Kurtosis-0.68160075
Mean1.1928978
Median Absolute Deviation (MAD)0
Skewness0.89316516
Sum122659.71
Variance2.4650142
MonotonicityNot monotonic
2023-02-21T11:56:40.356460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58649
57.0%
0.6931471806 2947
 
2.9%
1.098612289 2272
 
2.2%
1.386294361 2007
 
2.0%
1.609437912 1852
 
1.8%
1.791759469 1692
 
1.6%
1.945910149 1515
 
1.5%
2.079441542 1392
 
1.4%
2.197224577 1295
 
1.3%
2.302585093 1254
 
1.2%
Other values (204) 27950
27.2%
ValueCountFrequency (%)
0 58649
57.0%
0.6931471806 2947
 
2.9%
1.098612289 2272
 
2.2%
1.386294361 2007
 
2.0%
1.609437912 1852
 
1.8%
1.791759469 1692
 
1.6%
1.945910149 1515
 
1.5%
2.079441542 1392
 
1.4%
2.197224577 1295
 
1.3%
2.302585093 1254
 
1.2%
ValueCountFrequency (%)
5.375278408 1
 
< 0.1%
5.370638028 2
 
< 0.1%
5.361292166 5
< 0.1%
5.356586275 2
 
< 0.1%
5.351858133 2
 
< 0.1%
5.347107531 4
< 0.1%
5.33753808 1
 
< 0.1%
5.332718793 2
 
< 0.1%
5.327876169 3
< 0.1%
5.323009979 3
< 0.1%

Arrival Delay in Minutes
Real number (ℝ)

Distinct320
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2252343
Minimum0
Maximum5.4930614
Zeros58135
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:40.502326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5649494
95-th percentile4.2626799
Maximum5.4930614
Range5.4930614
Interquartile range (IQR)2.5649494

Descriptive statistics

Standard deviation1.5832851
Coefficient of variation (CV)1.2922305
Kurtosis-0.76444771
Mean1.2252343
Median Absolute Deviation (MAD)0
Skewness0.84631012
Sum125984.72
Variance2.5067918
MonotonicityNot monotonic
2023-02-21T11:56:40.635652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58135
56.5%
0.6931471806 2211
 
2.2%
1.098612289 2061
 
2.0%
1.386294361 1952
 
1.9%
1.609437912 1906
 
1.9%
1.791759469 1657
 
1.6%
1.945910149 1616
 
1.6%
2.079441542 1481
 
1.4%
2.197224577 1394
 
1.4%
2.302585093 1264
 
1.2%
Other values (310) 29148
28.3%
ValueCountFrequency (%)
0 58135
56.5%
0.5437442748 116
 
0.1%
0.6931471806 2211
 
2.2%
0.9942382277 9
 
< 0.1%
1.098612289 2061
 
2.0%
1.303696515 8
 
< 0.1%
1.386294361 1952
 
1.9%
1.539681752 3
 
< 0.1%
1.609437912 1906
 
1.9%
1.730473722 9
 
< 0.1%
ValueCountFrequency (%)
5.493061443 1
 
< 0.1%
5.472270674 1
 
< 0.1%
5.438079309 2
 
< 0.1%
5.429345629 1
 
< 0.1%
5.424950017 1
 
< 0.1%
5.420534999 1
 
< 0.1%
5.416100402 3
< 0.1%
5.407171771 3
< 0.1%
5.402677382 3
< 0.1%
5.393627546 6
< 0.1%

Gender_Female
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1.0
52169 
0.0
50656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 52169
50.7%
0.0 50656
49.3%

Length

2023-02-21T11:56:40.765353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:40.877378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 52169
50.7%
0.0 50656
49.3%

Most occurring characters

ValueCountFrequency (%)
0 153481
49.8%
. 102825
33.3%
1 52169
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 153481
74.6%
1 52169
 
25.4%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 153481
49.8%
. 102825
33.3%
1 52169
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 153481
49.8%
. 102825
33.3%
1 52169
 
16.9%

Gender_Male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
52169 
1.0
50656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 52169
50.7%
1.0 50656
49.3%

Length

2023-02-21T11:56:40.971982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:41.080230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 52169
50.7%
1.0 50656
49.3%

Most occurring characters

ValueCountFrequency (%)
0 154994
50.2%
. 102825
33.3%
1 50656
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154994
75.4%
1 50656
 
24.6%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 154994
50.2%
. 102825
33.3%
1 50656
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 154994
50.2%
. 102825
33.3%
1 50656
 
16.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1.0
84003 
0.0
18822 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 84003
81.7%
0.0 18822
 
18.3%

Length

2023-02-21T11:56:41.177067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:41.285299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 84003
81.7%
0.0 18822
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 121647
39.4%
. 102825
33.3%
1 84003
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 121647
59.2%
1 84003
40.8%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 121647
39.4%
. 102825
33.3%
1 84003
27.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 121647
39.4%
. 102825
33.3%
1 84003
27.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
84003 
1.0
18822 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 84003
81.7%
1.0 18822
 
18.3%

Length

2023-02-21T11:56:41.387637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:41.496248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84003
81.7%
1.0 18822
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 186828
60.6%
. 102825
33.3%
1 18822
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 186828
90.8%
1 18822
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 186828
60.6%
. 102825
33.3%
1 18822
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 186828
60.6%
. 102825
33.3%
1 18822
 
6.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1.0
70897 
0.0
31928 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 70897
68.9%
0.0 31928
31.1%

Length

2023-02-21T11:56:41.594612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:41.702615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 70897
68.9%
0.0 31928
31.1%

Most occurring characters

ValueCountFrequency (%)
0 134753
43.7%
. 102825
33.3%
1 70897
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134753
65.5%
1 70897
34.5%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134753
43.7%
. 102825
33.3%
1 70897
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134753
43.7%
. 102825
33.3%
1 70897
23.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
70897 
1.0
31928 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 70897
68.9%
1.0 31928
31.1%

Length

2023-02-21T11:56:41.797838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:41.915596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 70897
68.9%
1.0 31928
31.1%

Most occurring characters

ValueCountFrequency (%)
0 173722
56.3%
. 102825
33.3%
1 31928
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173722
84.5%
1 31928
 
15.5%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173722
56.3%
. 102825
33.3%
1 31928
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173722
56.3%
. 102825
33.3%
1 31928
 
10.4%

Class_Business
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
53700 
1.0
49125 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 53700
52.2%
1.0 49125
47.8%

Length

2023-02-21T11:56:42.017702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:42.140316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 53700
52.2%
1.0 49125
47.8%

Most occurring characters

ValueCountFrequency (%)
0 156525
50.7%
. 102825
33.3%
1 49125
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156525
76.1%
1 49125
 
23.9%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156525
50.7%
. 102825
33.3%
1 49125
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156525
50.7%
. 102825
33.3%
1 49125
 
15.9%

Class_Eco
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
56547 
1.0
46278 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 56547
55.0%
1.0 46278
45.0%

Length

2023-02-21T11:56:42.218389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:42.289418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 56547
55.0%
1.0 46278
45.0%

Most occurring characters

ValueCountFrequency (%)
0 159372
51.7%
. 102825
33.3%
1 46278
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159372
77.5%
1 46278
 
22.5%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 159372
51.7%
. 102825
33.3%
1 46278
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 159372
51.7%
. 102825
33.3%
1 46278
 
15.0%

Class_Eco Plus
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
95403 
1.0
 
7422

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 95403
92.8%
1.0 7422
 
7.2%

Length

2023-02-21T11:56:42.352472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:42.423543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 95403
92.8%
1.0 7422
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 198228
64.3%
. 102825
33.3%
1 7422
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 198228
96.4%
1 7422
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 198228
64.3%
. 102825
33.3%
1 7422
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 198228
64.3%
. 102825
33.3%
1 7422
 
2.4%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1609159 × 10-16
Minimum-2.1437384
Maximum3.0207795
Zeros0
Zeros (%)0.0%
Negative50869
Negative (%)49.5%
Memory size1.6 MiB
2023-02-21T11:56:42.493274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2.1437384
5-th percentile-1.680256
Q1-0.81950305
median0.041249948
Q30.7695794
95-th percentile1.6303324
Maximum3.0207795
Range5.164518
Interquartile range (IQR)1.5890825

Descriptive statistics

Standard deviation1.0000049
Coefficient of variation (CV)8.6139301 × 1015
Kurtosis-0.71989283
Mean1.1609159 × 10-16
Median Absolute Deviation (MAD)0.79454123
Skewness-0.0047534008
Sum1.1425527 × 10-11
Variance1.0000097
MonotonicityNot monotonic
2023-02-21T11:56:42.588382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.02496182074 2938
 
2.9%
-0.9519265841 2769
 
2.7%
0.04124994807 2544
 
2.5%
0.3060970233 2462
 
2.4%
0.1736734857 2431
 
2.4%
0.1074617169 2427
 
2.4%
-1.150561891 2338
 
2.3%
0.3723087921 2319
 
2.3%
-1.084350122 2318
 
2.3%
0.5047323297 2309
 
2.2%
Other values (65) 77970
75.8%
ValueCountFrequency (%)
-2.143738423 557
0.5%
-2.077526654 634
0.6%
-2.011314885 682
0.7%
-1.945103116 670
0.7%
-1.878891347 667
0.6%
-1.812679579 630
0.6%
-1.74646781 619
0.6%
-1.680256041 701
0.7%
-1.614044272 807
0.8%
-1.547832503 884
0.9%
ValueCountFrequency (%)
3.020779545 17
 
< 0.1%
2.6897207 75
 
0.1%
2.623508932 40
 
< 0.1%
2.557297163 30
 
< 0.1%
2.491085394 85
0.1%
2.424873625 45
 
< 0.1%
2.358661856 60
 
0.1%
2.292450088 44
 
< 0.1%
2.226238319 49
 
< 0.1%
2.16002655 198
0.2%

Inflight wifi service
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8109915
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:42.675370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2491096
Coefficient of variation (CV)0.4443662
Kurtosis-0.97438178
Mean2.8109915
Median Absolute Deviation (MAD)1
Skewness0.1658865
Sum289040.2
Variance1.5602748
MonotonicityNot monotonic
2023-02-21T11:56:42.749445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 25904
25.2%
2 25769
25.1%
4 19764
19.2%
1 17835
17.3%
5 11408
11.1%
2.6 285
 
0.3%
2.8 277
 
0.3%
2.4 231
 
0.2%
3.2 217
 
0.2%
2.2 181
 
0.2%
Other values (11) 954
 
0.9%
ValueCountFrequency (%)
1 17835
17.3%
1.2 84
 
0.1%
1.4 96
 
0.1%
1.6 95
 
0.1%
1.8 135
 
0.1%
2 25769
25.1%
2.2 181
 
0.2%
2.4 231
 
0.2%
2.6 285
 
0.3%
2.8 277
 
0.3%
ValueCountFrequency (%)
5 11408
11.1%
4.8 21
 
< 0.1%
4.6 28
 
< 0.1%
4.4 51
 
< 0.1%
4.2 37
 
< 0.1%
4 19764
19.2%
3.8 111
 
0.1%
3.6 139
 
0.1%
3.4 157
 
0.2%
3.2 217
 
0.2%
Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2397082
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:42.856324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.366745
Coefficient of variation (CV)0.42187286
Kurtosis-1.1704732
Mean3.2397082
Median Absolute Deviation (MAD)1
Skewness-0.2726
Sum333123
Variance1.8679918
MonotonicityNot monotonic
2023-02-21T11:56:42.932384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 25782
25.1%
5 22288
21.7%
3 18111
17.6%
2 17151
16.7%
1 15403
15.0%
4.2 530
 
0.5%
4.4 479
 
0.5%
3.8 424
 
0.4%
3.6 373
 
0.4%
3.2 346
 
0.3%
Other values (11) 1938
 
1.9%
ValueCountFrequency (%)
1 15403
15.0%
1.2 28
 
< 0.1%
1.4 29
 
< 0.1%
1.6 59
 
0.1%
1.8 99
 
0.1%
2 17151
16.7%
2.2 151
 
0.1%
2.4 209
 
0.2%
2.6 248
 
0.2%
2.8 263
 
0.3%
ValueCountFrequency (%)
5 22288
21.7%
4.8 198
 
0.2%
4.6 322
 
0.3%
4.4 479
 
0.5%
4.2 530
 
0.5%
4 25782
25.1%
3.8 424
 
0.4%
3.6 373
 
0.4%
3.4 332
 
0.3%
3.2 346
 
0.3%

Ease of Online booking
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8720895
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:43.017278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2859134
Coefficient of variation (CV)0.4477275
Kurtosis-1.0507267
Mean2.8720895
Median Absolute Deviation (MAD)1
Skewness0.12948706
Sum295322.6
Variance1.6535734
MonotonicityNot monotonic
2023-02-21T11:56:43.092549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 24594
23.9%
2 24063
23.4%
4 19558
19.0%
1 17553
17.1%
5 13753
13.4%
2.8 368
 
0.4%
2.6 362
 
0.4%
3.2 325
 
0.3%
2.4 320
 
0.3%
2.2 273
 
0.3%
Other values (11) 1656
 
1.6%
ValueCountFrequency (%)
1 17553
17.1%
1.2 191
 
0.2%
1.4 212
 
0.2%
1.6 182
 
0.2%
1.8 198
 
0.2%
2 24063
23.4%
2.2 273
 
0.3%
2.4 320
 
0.3%
2.6 362
 
0.4%
2.8 368
 
0.4%
ValueCountFrequency (%)
5 13753
13.4%
4.8 23
 
< 0.1%
4.6 46
 
< 0.1%
4.4 61
 
0.1%
4.2 93
 
0.1%
4 19558
19.0%
3.8 198
 
0.2%
3.6 203
 
0.2%
3.4 249
 
0.2%
3.2 325
 
0.3%

Gate location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9761089
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:43.169562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2777781
Coefficient of variation (CV)0.42934519
Kurtosis-1.0309226
Mean2.9761089
Median Absolute Deviation (MAD)1
Skewness-0.057977333
Sum306018.4
Variance1.6327168
MonotonicityNot monotonic
2023-02-21T11:56:43.234195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28270
27.5%
4 24153
23.5%
2 19272
18.7%
1 17399
16.9%
5 13730
13.4%
3.4 1
 
< 0.1%
ValueCountFrequency (%)
1 17399
16.9%
2 19272
18.7%
3 28270
27.5%
3.4 1
 
< 0.1%
4 24153
23.5%
5 13730
13.4%
ValueCountFrequency (%)
5 13730
13.4%
4 24153
23.5%
3.4 1
 
< 0.1%
3 28270
27.5%
2 19272
18.7%
1 17399
16.9%

Food and drink
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2062203
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:43.314372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3264098
Coefficient of variation (CV)0.4136989
Kurtosis-1.1589025
Mean3.2062203
Median Absolute Deviation (MAD)1
Skewness-0.14573015
Sum329679.6
Variance1.759363
MonotonicityNot monotonic
2023-02-21T11:56:43.398616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 24088
23.4%
5 22144
21.5%
3 22076
21.5%
2 21759
21.2%
1 12701
12.4%
2.6 8
 
< 0.1%
3.2 8
 
< 0.1%
2.4 8
 
< 0.1%
1.2 5
 
< 0.1%
2.2 5
 
< 0.1%
Other values (10) 23
 
< 0.1%
ValueCountFrequency (%)
1 12701
12.4%
1.2 5
 
< 0.1%
1.4 1
 
< 0.1%
1.6 2
 
< 0.1%
1.8 1
 
< 0.1%
2 21759
21.2%
2.2 5
 
< 0.1%
2.4 8
 
< 0.1%
2.6 8
 
< 0.1%
2.8 4
 
< 0.1%
ValueCountFrequency (%)
5 22144
21.5%
4.6 3
 
< 0.1%
4.4 3
 
< 0.1%
4.2 1
 
< 0.1%
4 24088
23.4%
3.8 4
 
< 0.1%
3.6 2
 
< 0.1%
3.4 2
 
< 0.1%
3.2 8
 
< 0.1%
3 22076
21.5%

Online boarding
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3130756
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:43.494687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2629614
Coefficient of variation (CV)0.38120512
Kurtosis-0.95459125
Mean3.3130756
Median Absolute Deviation (MAD)0.6
Skewness-0.32158953
Sum340667
Variance1.5950715
MonotonicityNot monotonic
2023-02-21T11:56:43.578562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 30547
29.7%
3 21837
21.2%
5 20479
19.9%
2 17483
17.0%
1 10629
 
10.3%
2.6 210
 
0.2%
2.8 192
 
0.2%
2.4 188
 
0.2%
3.2 170
 
0.2%
3.4 170
 
0.2%
Other values (11) 920
 
0.9%
ValueCountFrequency (%)
1 10629
10.3%
1.2 122
 
0.1%
1.4 117
 
0.1%
1.6 94
 
0.1%
1.8 105
 
0.1%
2 17483
17.0%
2.2 134
 
0.1%
2.4 188
 
0.2%
2.6 210
 
0.2%
2.8 192
 
0.2%
ValueCountFrequency (%)
5 20479
19.9%
4.8 16
 
< 0.1%
4.6 27
 
< 0.1%
4.4 31
 
< 0.1%
4.2 61
 
0.1%
4 30547
29.7%
3.8 82
 
0.1%
3.6 131
 
0.1%
3.4 170
 
0.2%
3.2 170
 
0.2%

Seat comfort
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4.0
31470 
5.0
26214 
3.0
18497 
2.0
14721 
1.0
11923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
4.0 31470
30.6%
5.0 26214
25.5%
3.0 18497
18.0%
2.0 14721
14.3%
1.0 11923
 
11.6%

Length

2023-02-21T11:56:43.674575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:43.757661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 31470
30.6%
5.0 26214
25.5%
3.0 18497
18.0%
2.0 14721
14.3%
1.0 11923
 
11.6%

Most occurring characters

ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 31470
 
10.2%
5 26214
 
8.5%
3 18497
 
6.0%
2 14721
 
4.8%
1 11923
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102825
50.0%
4 31470
 
15.3%
5 26214
 
12.7%
3 18497
 
9.0%
2 14721
 
7.2%
1 11923
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 31470
 
10.2%
5 26214
 
8.5%
3 18497
 
6.0%
2 14721
 
4.8%
1 11923
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 31470
 
10.2%
5 26214
 
8.5%
3 18497
 
6.0%
2 14721
 
4.8%
1 11923
 
3.9%

Inflight entertainment
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3601323
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:43.838027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3330517
Coefficient of variation (CV)0.39672596
Kurtosis-1.0637003
Mean3.3601323
Median Absolute Deviation (MAD)1
Skewness-0.36566781
Sum345505.6
Variance1.7770268
MonotonicityNot monotonic
2023-02-21T11:56:43.905243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 29144
28.3%
5 25021
24.3%
3 18831
18.3%
2 17486
17.0%
1 12334
12.0%
2.8 2
 
< 0.1%
3.8 2
 
< 0.1%
3.6 1
 
< 0.1%
3.4 1
 
< 0.1%
2.4 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 12334
12.0%
1.2 1
 
< 0.1%
1.8 1
 
< 0.1%
2 17486
17.0%
2.4 1
 
< 0.1%
2.8 2
 
< 0.1%
3 18831
18.3%
3.4 1
 
< 0.1%
3.6 1
 
< 0.1%
3.8 2
 
< 0.1%
ValueCountFrequency (%)
5 25021
24.3%
4 29144
28.3%
3.8 2
 
< 0.1%
3.6 1
 
< 0.1%
3.4 1
 
< 0.1%
3 18831
18.3%
2.8 2
 
< 0.1%
2.4 1
 
< 0.1%
2 17486
17.0%
1.8 1
 
< 0.1%

On-board service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4.0
30637 
5.0
23425 
3.0
22579 
2.0
14448 
1.0
11736 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 30637
29.8%
5.0 23425
22.8%
3.0 22579
22.0%
2.0 14448
14.1%
1.0 11736
 
11.4%

Length

2023-02-21T11:56:43.984471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:44.064471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 30637
29.8%
5.0 23425
22.8%
3.0 22579
22.0%
2.0 14448
14.1%
1.0 11736
 
11.4%

Most occurring characters

ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 30637
 
9.9%
5 23425
 
7.6%
3 22579
 
7.3%
2 14448
 
4.7%
1 11736
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102825
50.0%
4 30637
 
14.9%
5 23425
 
11.4%
3 22579
 
11.0%
2 14448
 
7.0%
1 11736
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 30637
 
9.9%
5 23425
 
7.6%
3 22579
 
7.3%
2 14448
 
4.7%
1 11736
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 30637
 
9.9%
5 23425
 
7.6%
3 22579
 
7.3%
2 14448
 
4.7%
1 11736
 
3.8%

Leg room service
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.359751
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:44.142522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3000372
Coefficient of variation (CV)0.38694451
Kurtosis-1.0608949
Mean3.359751
Median Absolute Deviation (MAD)1
Skewness-0.31405754
Sum345466.4
Variance1.6900967
MonotonicityNot monotonic
2023-02-21T11:56:44.583220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 28406
27.6%
5 24406
23.7%
3 19875
19.3%
2 19459
18.9%
1 10320
 
10.0%
2.4 38
 
< 0.1%
2.6 37
 
< 0.1%
2.2 37
 
< 0.1%
2.8 34
 
< 0.1%
1.8 33
 
< 0.1%
Other values (11) 180
 
0.2%
ValueCountFrequency (%)
1 10320
10.0%
1.2 17
 
< 0.1%
1.4 22
 
< 0.1%
1.6 21
 
< 0.1%
1.8 33
 
< 0.1%
2 19459
18.9%
2.2 37
 
< 0.1%
2.4 38
 
< 0.1%
2.6 37
 
< 0.1%
2.8 34
 
< 0.1%
ValueCountFrequency (%)
5 24406
23.7%
4.8 4
 
< 0.1%
4.6 3
 
< 0.1%
4.4 6
 
< 0.1%
4.2 11
 
< 0.1%
4 28406
27.6%
3.8 24
 
< 0.1%
3.6 20
 
< 0.1%
3.4 30
 
< 0.1%
3.2 22
 
< 0.1%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4.0
36962 
5.0
26849 
3.0
20401 
2.0
11420 
1.0
7193 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 36962
35.9%
5.0 26849
26.1%
3.0 20401
19.8%
2.0 11420
 
11.1%
1.0 7193
 
7.0%

Length

2023-02-21T11:56:44.676528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:44.764584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 36962
35.9%
5.0 26849
26.1%
3.0 20401
19.8%
2.0 11420
 
11.1%
1.0 7193
 
7.0%

Most occurring characters

ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 36962
 
12.0%
5 26849
 
8.7%
3 20401
 
6.6%
2 11420
 
3.7%
1 7193
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102825
50.0%
4 36962
 
18.0%
5 26849
 
13.1%
3 20401
 
9.9%
2 11420
 
5.6%
1 7193
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 36962
 
12.0%
5 26849
 
8.7%
3 20401
 
6.6%
2 11420
 
3.7%
1 7193
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 36962
 
12.0%
5 26849
 
8.7%
3 20401
 
6.6%
2 11420
 
3.7%
1 7193
 
2.3%

Checkin service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4.0
28761 
3.0
28248 
5.0
20362 
1.0
12737 
2.0
12717 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 28761
28.0%
3.0 28248
27.5%
5.0 20362
19.8%
1.0 12737
12.4%
2.0 12717
12.4%

Length

2023-02-21T11:56:44.839665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:44.922225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 28761
28.0%
3.0 28248
27.5%
5.0 20362
19.8%
1.0 12737
12.4%
2.0 12717
12.4%

Most occurring characters

ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 28761
 
9.3%
3 28248
 
9.2%
5 20362
 
6.6%
1 12737
 
4.1%
2 12717
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102825
50.0%
4 28761
 
14.0%
3 28248
 
13.7%
5 20362
 
9.9%
1 12737
 
6.2%
2 12717
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 28761
 
9.3%
3 28248
 
9.2%
5 20362
 
6.6%
1 12737
 
4.1%
2 12717
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 28761
 
9.3%
3 28248
 
9.2%
5 20362
 
6.6%
1 12737
 
4.1%
2 12717
 
4.1%

Inflight service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4.0
37676 
5.0
26922 
3.0
19977 
2.0
11314 
1.0
6936 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 37676
36.6%
5.0 26922
26.2%
3.0 19977
19.4%
2.0 11314
 
11.0%
1.0 6936
 
6.7%

Length

2023-02-21T11:56:45.005281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:45.094749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 37676
36.6%
5.0 26922
26.2%
3.0 19977
19.4%
2.0 11314
 
11.0%
1.0 6936
 
6.7%

Most occurring characters

ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 37676
 
12.2%
5 26922
 
8.7%
3 19977
 
6.5%
2 11314
 
3.7%
1 6936
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205650
66.7%
Other Punctuation 102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102825
50.0%
4 37676
 
18.3%
5 26922
 
13.1%
3 19977
 
9.7%
2 11314
 
5.5%
1 6936
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 37676
 
12.2%
5 26922
 
8.7%
3 19977
 
6.5%
2 11314
 
3.7%
1 6936
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 102825
33.3%
0 102825
33.3%
4 37676
 
12.2%
5 26922
 
8.7%
3 19977
 
6.5%
2 11314
 
3.7%
1 6936
 
2.2%

Cleanliness
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2864692
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:56:45.171643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3112847
Coefficient of variation (CV)0.39899498
Kurtosis-1.0128222
Mean3.2864692
Median Absolute Deviation (MAD)1
Skewness-0.29899112
Sum337931.2
Variance1.7194677
MonotonicityNot monotonic
2023-02-21T11:56:45.238171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 26885
26.1%
3 24377
23.7%
5 22432
21.8%
2 15956
15.5%
1 13166
12.8%
2.4 2
 
< 0.1%
3.2 2
 
< 0.1%
2.8 1
 
< 0.1%
3.6 1
 
< 0.1%
1.6 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 13166
12.8%
1.2 1
 
< 0.1%
1.6 1
 
< 0.1%
1.8 1
 
< 0.1%
2 15956
15.5%
2.4 2
 
< 0.1%
2.8 1
 
< 0.1%
3 24377
23.7%
3.2 2
 
< 0.1%
3.6 1
 
< 0.1%
ValueCountFrequency (%)
5 22432
21.8%
4 26885
26.1%
3.6 1
 
< 0.1%
3.2 2
 
< 0.1%
3 24377
23.7%
2.8 1
 
< 0.1%
2.4 2
 
< 0.1%
2 15956
15.5%
1.8 1
 
< 0.1%
1.6 1
 
< 0.1%

satisfaction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
neutral or dissatisfied
58226 
satisfied
44599 

Length

Max length23
Median length23
Mean length16.927683
Min length9

Characters and Unicode

Total characters1740589
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowneutral or dissatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied 58226
56.6%
satisfied 44599
43.4%

Length

2023-02-21T11:56:45.320225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:56:45.395287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
neutral 58226
26.6%
or 58226
26.6%
dissatisfied 58226
26.6%
satisfied 44599
20.3%

Most occurring characters

ValueCountFrequency (%)
i 263876
15.2%
s 263876
15.2%
e 161051
9.3%
t 161051
9.3%
a 161051
9.3%
d 161051
9.3%
r 116452
6.7%
116452
6.7%
f 102825
 
5.9%
n 58226
 
3.3%
Other values (3) 174678
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1624137
93.3%
Space Separator 116452
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 263876
16.2%
s 263876
16.2%
e 161051
9.9%
t 161051
9.9%
a 161051
9.9%
d 161051
9.9%
r 116452
7.2%
f 102825
 
6.3%
n 58226
 
3.6%
u 58226
 
3.6%
Other values (2) 116452
7.2%
Space Separator
ValueCountFrequency (%)
116452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1624137
93.3%
Common 116452
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 263876
16.2%
s 263876
16.2%
e 161051
9.9%
t 161051
9.9%
a 161051
9.9%
d 161051
9.9%
r 116452
7.2%
f 102825
 
6.3%
n 58226
 
3.6%
u 58226
 
3.6%
Other values (2) 116452
7.2%
Common
ValueCountFrequency (%)
116452
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1740589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 263876
15.2%
s 263876
15.2%
e 161051
9.3%
t 161051
9.3%
a 161051
9.3%
d 161051
9.3%
r 116452
6.7%
116452
6.7%
f 102825
 
5.9%
n 58226
 
3.3%
Other values (3) 174678
10.0%

Interactions

2023-02-21T11:56:31.046211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:55:58.957497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:00.976488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:02.953669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:05.066523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:07.034451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:09.307405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:11.444395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:13.738380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:16.178478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:19.092410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:21.290928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:25.230244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:31.401266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:55:59.097598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:01.102226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:03.091568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:05.204504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:07.184342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:09.458565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:11.598477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:13.951982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:16.331215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:19.253841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:21.549396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:25.567627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:32.157423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:55:59.237842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:01.244305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:03.350314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:05.347561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:07.340326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:09.615259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:11.756664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:14.172161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:16.696633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:19.406001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:21.812199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:25.918589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:32.512196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:55:59.375490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:01.382273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:03.487238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:05.473211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:07.486938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:09.765566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:11.907313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:14.390718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:16.848396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:19.556150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:22.071291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:26.301350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:32.884576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:55:59.521633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:01.530348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:03.637476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:05.619268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:07.628258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:09.923630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:12.069413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:14.619615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:17.010568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:19.719656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:22.334547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:26.657344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:33.265238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:55:59.680561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:01.691307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:03.801479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:05.773301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:07.792371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:10.076445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:12.240237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:14.840635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:17.185015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:19.890353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:22.625394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:27.032639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:33.648430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:55:59.846638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:01.852831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:03.960615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:05.930613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:07.964473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:10.243451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:12.393550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:15.017573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:17.447769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:20.062608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:22.920600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:27.538225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:34.043203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:00.009398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:02.014033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:04.119622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:06.090642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:08.136359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:10.415262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:12.562346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:15.171148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:17.685237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:20.232252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:23.212600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:28.114864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:34.418220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:00.170844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:02.173537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:04.278239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:06.251017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:08.308398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:10.586247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:12.743189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:15.337270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:17.906657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:20.401206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:23.498212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:28.682903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:34.790542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:00.333591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:02.329623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:04.435581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:06.411604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:08.477835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:10.754404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:12.914522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:15.502406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:18.154346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:20.553324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:23.784453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:29.256230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:35.154242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:00.497509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:02.488593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:04.594200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:06.564438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:08.645256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:10.922478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:13.084167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:15.672200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:18.401361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:20.722918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:24.043586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:29.831290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:35.531553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:00.661533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:02.649268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:04.753366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:06.721153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:08.812492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:11.100419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:13.268731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:15.845366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:18.648395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:20.891842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:24.422181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:30.290682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:35.865525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:00.825289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:02.807832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:04.918536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:06.876220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:09.144597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:11.280249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:13.509334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:16.016500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:18.890459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:21.063300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:24.860079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:56:30.672839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2023-02-21T11:56:36.787255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-21T11:56:38.693705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Flight DistanceDeparture Delay in MinutesArrival Delay in MinutesGender_FemaleGender_MaleCustomer Type_Loyal CustomerCustomer Type_disloyal CustomerType of Travel_Business travelType of Travel_Personal TravelClass_BusinessClass_EcoClass_Eco PlusAgeInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinesssatisfaction
06.1333983.2580972.9444390.01.01.00.00.01.00.00.01.0-1.7464683.04.03.01.05.03.05.05.04.03.04.04.05.05.0neutral or dissatisfied
15.4638320.6931471.945910.01.00.01.01.00.01.00.00.0-0.9519273.02.03.03.01.03.01.01.01.05.03.01.04.01.0neutral or dissatisfied
27.0414120.00.01.00.01.00.01.00.01.00.00.0-0.8857152.02.02.02.05.05.05.05.04.03.04.04.04.05.0satisfied
36.333282.4849072.3025851.00.01.00.01.00.01.00.00.0-0.9519272.05.05.05.02.02.02.02.02.05.03.01.04.02.0neutral or dissatisfied
45.3706380.00.00.01.01.00.01.00.01.00.00.01.4316973.03.03.03.04.05.05.03.03.04.04.03.03.03.0satisfied
57.0741170.00.01.00.01.00.00.01.00.01.00.0-0.8857153.04.02.01.01.02.01.01.03.04.04.04.04.01.0neutral or dissatisfied
67.1522692.3025853.1780540.01.01.00.00.01.00.01.00.00.5047322.04.02.03.02.02.02.02.03.03.04.03.05.02.0neutral or dissatisfied
77.6187421.6094380.01.00.01.00.01.00.01.00.00.00.8357914.03.04.04.05.05.05.05.05.05.05.04.05.04.0satisfied
86.7499310.00.01.00.01.00.01.00.01.00.00.00.1074621.02.02.02.04.03.03.01.01.02.01.04.01.02.0neutral or dissatisfied
96.9679090.00.00.01.00.01.01.00.00.01.00.0-1.2829853.03.03.04.02.03.03.02.02.03.04.04.03.02.0neutral or dissatisfied
Flight DistanceDeparture Delay in MinutesArrival Delay in MinutesGender_FemaleGender_MaleCustomer Type_Loyal CustomerCustomer Type_disloyal CustomerType of Travel_Business travelType of Travel_Personal TravelClass_BusinessClass_EcoClass_Eco PlusAgeInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinesssatisfaction
1038946.5694812.8903723.2958370.01.01.00.01.00.01.00.00.0-0.8857154.04.04.04.05.05.05.05.03.04.04.03.04.05.0satisfied
1038956.9622432.6390572.3978951.00.00.01.01.00.00.01.00.0-1.0181381.01.01.02.01.01.01.01.03.03.05.05.04.01.0neutral or dissatisfied
1038966.7661920.00.00.01.01.00.01.00.00.01.00.01.166854.05.05.05.04.04.04.04.03.04.03.01.03.04.0neutral or dissatisfied
1038977.3777592.3025852.0794421.00.01.00.01.00.01.00.00.01.3654855.05.05.05.05.05.04.04.04.04.04.04.04.04.0satisfied
1038987.3907990.00.00.01.01.00.00.01.00.01.00.00.7033683.01.03.04.02.03.02.02.04.03.04.02.04.02.0neutral or dissatisfied
1038995.262691.3862940.01.00.00.01.01.00.00.01.00.0-1.084352.01.02.03.02.02.02.02.03.01.04.02.03.02.0neutral or dissatisfied
1039007.7613190.00.00.01.01.00.01.00.01.00.00.00.6371564.04.04.04.02.04.05.05.05.05.05.05.05.04.0satisfied
1039017.59892.0794422.708050.01.00.01.01.00.01.00.00.0-0.6208681.01.01.03.04.01.05.04.03.02.04.05.05.04.0neutral or dissatisfied
1039026.9087550.00.01.00.00.01.01.00.00.01.00.0-1.1505621.01.01.05.01.01.01.01.04.05.01.05.04.01.0neutral or dissatisfied
1039037.4524020.00.00.01.01.00.01.00.01.00.00.0-0.8195031.03.03.03.01.01.01.01.01.01.04.04.03.01.0neutral or dissatisfied